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BACKGROUND: Accurate volumetric segmentation of primary central nervous system lymphoma (PCNSL) is essential for assessing and monitoring the tumor before radiotherapy and the treatment planning. The tedious manual segmentation leads to interindividual and intraindividual differences, while existing automatic segmentation methods cause under-segmentation of PCNSL due to the complex and multifaceted nature of the tumor. OBJECTIVE: To address the challenges of small size, diffused distribution, poor inter-layer continuity on the same axis, and tendency for over-segmentation in brain MRI PCNSL segmentation, we propose an improved attention module based on nnUNet for automated segmentation. METHODS: We collected 114 T1 MRI images of patients in the Huashan Hospital, Shanghai. Then randomly split the total of 114 cases into 5 distinct training and test sets for a 5-fold cross-validation. To efficiently and accurately delineate the PCNSL, we proposed an improved attention module based on nnU-Net with 3D convolutions, batch normalization, and residual attention (res-attention) to learn the tumor region information. Additionally, multi-scale dilated convolution kernels with different dilation rates were integrated to broaden the receptive field. We further used attentional feature fusion with 3D convolutions (AFF3D) to fuse the feature maps generated by multi-scale dilated convolution kernels to reduce under-segmentation. RESULTS: Compared to existing methods, our attention module improves the ability to distinguish diffuse and edge enhanced types of tumors; and the broadened receptive field captures tumor features of various scales and shapes more effectively, achieving a 0.9349 Dice Similarity Coefficient (DSC). CONCLUSIONS: Quantitative results demonstrate the effectiveness of the proposed method in segmenting the PCNSL. To our knowledge, this is the first study to introduce attention modules into deep learning for segmenting PCNSL based on brain magnetic resonance imaging (MRI), promoting the localization of PCNSL before radiotherapy. [ABSTRACT FROM AUTHOR]

In single photon emission computed tomography, it is a challenging task to maintain reasonable performance using only one specific collimator for radio-tracers over a broad spectrum of diagnostic photon energies, since photon scatter and penetration in a collimator differ with the photon energy. Frequent collimator exchanges are inevitable in daily clinical SPECT imaging, which hinders throughput while subjecting the camera to operational errors and damage. Our objective is to design a collimator, which independent of the photon energy performs reasonably well for commonly used radiotracers with low- to medium-energy levels of gamma emissions. Using the Geant4 simulation toolkit, we simulated and evaluated a parallel-hole collimator mounted to a CZT detector. With the pixel-geometry-matching collimation, the pitch of the collimator hole was fixed to match the pixel size of the CZT detector throughout this work. Four variables, hole shape, hole length, hole radius/width and the source-to-collimator distance were carefully studied. Scatter and penetration of the collimator, sensitivity and spatial resolution of the system were assessed for four radionuclides including 57Co, 99m Tc, 123I and 111In, with respect to the aforementioned four variables. An optimal collimator was then decided upon such that it maximized the total relative sensitivity (TRS) for the four considered radionuclides while other performance parameters, such as scatter, penetration and spatial resolution, were benchmarked to prevalent commercial scanners and collimators. Digital phantom studies were also performed to validate the system with the optimal square-hole collimator (23 mm hole length, 1.28 mm hole width, 0.32 mm septal thickness) in terms of contrast, contrast-to-noise ratio and recovery ratio. This study demonstrates promise of our proposed energy-optimized collimator to be used in a CZT-based gamma camera, with comparable or even better imaging performance versus commercial collimators such as low-energy high resolution (LEHR) and medium energy general purpose (MEGP) collimators.

PurposeSeveral new technologies for single photon emission computed tomography (SPECT) instrumentation with parallel-hole collimation have been proposed to improve detector sensitivity and signal collection efficiency. Benefits from improved signal efficiency include shorter acquisition times and lower dose requirements. In this paper, the authors show a possibility of over an order of magnitude enhancement in photon detection efficiency (from 7.6 × 10(-5) to 1.6 × 10(-3)) for dopamine transporter (DaT) imaging of the striatum over the conventional SPECT parallel-hole collimators by use of custom-designed 20 multipinhole (20-MPH) collimators with apertures of 0.75 cm diameter.MethodsQuantifying specific binding ratio (SBR) of (123)I-ioflupane or (123)I-iometopane's signal at the striatal region is a common brain imaging method to confirm the diagnosis of the Parkinson's disease. The authors performed imaging of a striatal phantom filled with aqueous solution of I-123 and compared camera recovery ratios of SBR acquired between low-energy high-resolution (LEHR) parallel-hole collimators and 20-MPH collimators.ResultsWith only two-thirds of total acquisition time (20 min against 30 min), a comparable camera recovery ratio of SBR was achieved using 20-MPH collimators in comparison to that from the LEHR collimator study.ConclusionsTheir systematic analyses showed that the 20-MPH collimator could be a promising alternative for the DaT SPECT imaging for brain over the traditional LEHR collimator, which could give both shorter scan time and improved diagnostic accuracy.